193 lines
6.8 KiB
Python
193 lines
6.8 KiB
Python
# Copyright 2025 SGLang Team
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""LFM2-MoE (Liquid Foundation Model 2 - Mixture of Experts) configuration
|
|
|
|
Note: HF transformers has Lfm2MoeConfig in v5.0.0rc2 (unreleased).
|
|
Once released, we could inherit from it like Lfm2Config does with HFLfm2Config.
|
|
For now, we define a standalone config to support the model immediately.
|
|
"""
|
|
|
|
from typing import List, Optional
|
|
|
|
from transformers import CONFIG_MAPPING
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
|
|
from sglang.srt.configs.mamba_utils import Mamba2CacheParams, Mamba2StateShape
|
|
|
|
|
|
class Lfm2MoeConfig(PretrainedConfig):
|
|
"""
|
|
Configuration for LFM2-MoE models (e.g., LiquidAI/LFM2-8B-A1B).
|
|
|
|
LFM2-MoE is a hybrid architecture with:
|
|
- Attention layers and ShortConv layers (like dense LFM2)
|
|
- MoE (Mixture of Experts) FFN layers with sigmoid routing
|
|
|
|
Key MoE specifics:
|
|
- First `num_dense_layers` use dense MLP, rest use MoE
|
|
- Sigmoid routing (not softmax) with expert_bias for load balancing
|
|
- expert_bias is fp32 for numerical stability
|
|
"""
|
|
|
|
model_type = "lfm2_moe"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size: int = 65536,
|
|
hidden_size: int = 2048,
|
|
intermediate_size: int = 7168,
|
|
moe_intermediate_size: int = 1792,
|
|
num_hidden_layers: int = 32,
|
|
num_attention_heads: int = 32,
|
|
num_key_value_heads: int = 8,
|
|
max_position_embeddings: int = 128000,
|
|
initializer_range: float = 0.02,
|
|
norm_eps: float = 1e-5,
|
|
use_cache: bool = True,
|
|
pad_token_id: int = 0,
|
|
bos_token_id: int = 1,
|
|
eos_token_id: int = 2,
|
|
tie_word_embeddings: bool = True,
|
|
rope_parameters: Optional[dict] = None,
|
|
conv_bias: bool = False,
|
|
conv_L_cache: int = 3,
|
|
# MoE-specific parameters
|
|
num_dense_layers: int = 2,
|
|
num_experts: int = 32,
|
|
num_experts_per_tok: int = 4,
|
|
use_expert_bias: bool = True,
|
|
routed_scaling_factor: float = 1.0,
|
|
norm_topk_prob: bool = True,
|
|
# Layer types
|
|
layer_types: Optional[List[str]] = None,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.moe_intermediate_size = moe_intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.initializer_range = initializer_range
|
|
self.norm_eps = norm_eps
|
|
self.use_cache = use_cache
|
|
|
|
# Conv parameters
|
|
self.conv_bias = conv_bias
|
|
self.conv_L_cache = conv_L_cache
|
|
|
|
# MoE parameters
|
|
self.num_dense_layers = num_dense_layers
|
|
self.num_experts = num_experts
|
|
self.num_experts_per_tok = num_experts_per_tok
|
|
self.use_expert_bias = use_expert_bias
|
|
self.routed_scaling_factor = routed_scaling_factor
|
|
self.norm_topk_prob = norm_topk_prob
|
|
|
|
# Layer types (attention vs conv)
|
|
self.layer_types = layer_types
|
|
|
|
# RoPE parameters
|
|
self.rope_parameters = rope_parameters
|
|
|
|
# Validate layer_types length matches num_hidden_layers
|
|
if layer_types is not None and len(layer_types) != num_hidden_layers:
|
|
raise ValueError(
|
|
f"layer_types length ({len(layer_types)}) must match "
|
|
f"num_hidden_layers ({num_hidden_layers})"
|
|
)
|
|
|
|
# Handle tie_embedding alias from original config
|
|
tie_word_embeddings = kwargs.pop("tie_embedding", tie_word_embeddings)
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=tie_word_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
@property
|
|
def full_attention_layer_ids(self) -> List[int]:
|
|
"""Return indices of attention layers for KV cache."""
|
|
if self.layer_types is None:
|
|
return []
|
|
return [i for i, lt in enumerate(self.layer_types) if lt == "full_attention"]
|
|
|
|
@property
|
|
def linear_layer_ids(self) -> List[int]:
|
|
"""Return indices of conv layers for conv state cache."""
|
|
if self.layer_types is None:
|
|
return []
|
|
return [
|
|
i for i, lt in enumerate(self.layer_types) if lt in ("conv", "short_conv")
|
|
]
|
|
|
|
@property
|
|
def mamba_chunk_size(self) -> int:
|
|
"""Return chunk size for Mamba2 backend. LFM2 doesn't use chunking."""
|
|
return 1
|
|
|
|
@property
|
|
def mamba2_cache_params(self) -> Optional[Mamba2CacheParams]:
|
|
"""
|
|
Get cache params for HybridReqToTokenPool initialization.
|
|
|
|
LFM2-MoE uses ShortConv layers with a small fixed-size cache.
|
|
"""
|
|
from sglang.srt.layers.dp_attention import get_attention_tp_size
|
|
|
|
conv_layer_ids = self.linear_layer_ids
|
|
if not conv_layer_ids:
|
|
return None
|
|
|
|
hidden_size = self.hidden_size
|
|
# conv_L_cache in config is kernel_size (e.g., 3)
|
|
conv_kernel = int(self.conv_L_cache)
|
|
# actual cache size is kernel_size - 1 (e.g., 2 for kernel=3)
|
|
|
|
try:
|
|
tp_size = get_attention_tp_size()
|
|
except (AssertionError, RuntimeError):
|
|
tp_size = 1
|
|
|
|
shape = Mamba2StateShape.create(
|
|
tp_world_size=tp_size,
|
|
intermediate_size=hidden_size,
|
|
n_groups=1,
|
|
num_heads=tp_size, # Ensures divide works; temporal state is empty anyway
|
|
head_dim=hidden_size,
|
|
state_size=0,
|
|
conv_kernel=conv_kernel,
|
|
)
|
|
|
|
# Uses default mamba2_state_dtype() which reads SGLANG_MAMBA_CONV_DTYPE env var
|
|
# (defaults to bfloat16). Set SGLANG_MAMBA_CONV_DTYPE=float16 for fp16 inference.
|
|
return Mamba2CacheParams(
|
|
shape=shape,
|
|
layers=conv_layer_ids,
|
|
)
|
|
|
|
|
|
# Register with transformers CONFIG_MAPPING so AutoConfig.from_pretrained()
|
|
# can instantiate our config class when loading models with model_type="lfm2_moe"
|
|
try:
|
|
CONFIG_MAPPING.register("lfm2_moe", Lfm2MoeConfig)
|
|
except Exception:
|
|
# Already registered or registration failed - use direct assignment
|
|
CONFIG_MAPPING._extra_content["lfm2_moe"] = Lfm2MoeConfig
|